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Problems for assignment 3 of the probability theory ii course offered in spring 2003. The problems involve independent and identically distributed random variables, convergence in probability, and orthogonal random variables. Students are required to turn in the first three problems, while the remaining problems are optional. Hints are provided for some problems.
Typology: Assignments
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Turn in the first three problems. The remaining problems are optional problems that you may wish to
try, but they will not be collected. Other optional problems from Section 22 of Billingsley are listed on the
course web page.
every x:
P
max 1 ≤k≤n
Sk ≥ x
Sn ≥ x −
2 n
Hint: Let τ be the first time k at which Sk ≥ x and show that
Sn ≥ x −
2 n
∑^ n
k=
τ = k
Sn − Sk ≥ −
2 n
∑n k=1 Xk, it is easy to show that
Sn
n
a.s. −→ 0 implies
max 1 ≤k≤n|Sk|
n
a.s. −→ 0
Suppose that X 1 , X 2 ,... are independent. Show in this case that
Sn
n
P → 0 implies
max 1 ≤k≤n|Sk|
n
P → 0.
Hint: Use Etemadi’s inequality.
(i)
n=1 Xn^ <^ ∞^ a.s..
(ii)
n=
P (Xn > 1) + E[XnI[Xn≤1]]
(iii)
n=1 E
Xn/(1 + Xn)
max 1 ≤k≤n
Sk ≥ 2 α
× min 1 ≤j≤n
∣Sn − Sj
∣ (^) ≤ α
|Sn| ≥ α
Hint: Consider sets of the form
Sj < 2 α, 1 ≤ j ≤ k − 1 , Sk ≥ 2 α, |Sn − Sk| < α
n
E(|Xn|) < ∞,
then
n Xn^ converges absolutely a.s.
tions:
P [Xn = n^2 ] = P [Xn = −n^2 ] = n−^2
and
P [Xn = (−1)n] = 1 − 2 n−^2.
Prove whether or not n−^1
∑n k=2 Xk^ converges
(a) almost surely as n → ∞;
(b) in probability as n → ∞.
Characterize any limits which exist.
E(XmXn) = 0 for all m 6 = n). Set Sn =
∑n j=1 Xj^ ,^ n^ ≥^1. Prove that if^
n=1 EX
2 n <^ ∞, then there exists a random variable S such that
Sn
L^2 −→ S.
{ (^) ∑n
j=
Xj converges
= p.
Why doesn’t this problem contradict the Kolmogorov 0-1 law?